caffe：自己的数据训练模型 Label Image(三)

来源：互联网发布：视频调色知乎编辑：程序博客网时间：2024/05/17 04:20

（3）给数据做标签：trainData：valData = 7:3

随机将数据分开再Label Image:

txt格式，例如：plane/plane_0.jpg2

from __future__ import division

import os import randomimport cv2

#将TrainDataFinal里面的数据分别移到FinalTrain和FinalVal里面

DataPath = "TrainDataFinal/"TrainSavePath = "FinalTrain/"ValSavePath = "FianlVal/"for root, dirs, files, in os.walk(DataPath):N = 0 for name in files:FileFullPath = os.path.join(root, name)folder = root.split('/')[-1]cls = folder[-1]random.shuffle(files)fileNub = len(files)ValNub = fileNub*3/10if N <= ValNub:img = cv2.imread(FileFullPath)ValPath = ValSavePath + folderif not os.path.exists(ValPath):os.mkdir(ValPath)cv2.imwrite(ValPath + '/' + name, img)with open("FinalAnno/val.txt",'a') as f:f.write(folder[:-1] + '/' + name + '\t' + cls + '\n')N += 1else:img = cv2.imread(FileFullPath)TrainPath = TrainSavePath + folderif not os.path.exists(TrainPath):os.mkdir(TrainPath)cv2.imwrite(TrainPath + '/' + name, img)with open("FinalAnno/train.txt",'a') as f:f.write(folder[:-1] + '/' + name + '\t' + cls + '\n')N += 1print N

0 0